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A methodology to assess vessel berthing and speed optimization policies

Author

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  • J Fernando Alvarez

    (Det Norske Veritas, Høvik 1322, Norway. E-mails: Tore.Longva@dnv.com; ErnaSenkina.Engebrethsen@fks.fmcti.com)

  • Tore Longva

    (Det Norske Veritas, Høvik 1322, Norway. E-mails: Tore.Longva@dnv.com; ErnaSenkina.Engebrethsen@fks.fmcti.com)

  • Erna S Engebrethsen

    (Det Norske Veritas, Høvik 1322, Norway. E-mails: Tore.Longva@dnv.com; ErnaSenkina.Engebrethsen@fks.fmcti.com)

Abstract

Standard ocean shipping contracts stipulate that a chartered vessel must sail at ‘utmost despatch’, with no consideration for the availability of berths at the destination port. The berthing policies used at many ports, which admit vessels on a first-come, first-served basis, provide an additional incentive for the master to sail at full speed. These legacy contracts and berthing policies constitute a major driver of harbour congestion and marine fuel consumption, with adverse economic, safety, and environmental consequences. We propose a methodology to evaluate the potential benefits of new berthing policies and ocean shipping contracts. Given the importance of stochasticity on the performance of maritime transport systems, and the need to represent the efficient allocation of terminal resources, we have chosen a hybrid simulation-optimization approach. Our discrete event simulation model represents vessels and their principal economic and physical characteristics, the spatial layout of the terminal, performance of the land-side equipment, contractual agreements and associated penalties, and berthing policies. The proposed optimization model – a substantial extension of the traditional berth assignment problem – represents the logic of the terminal planner. The simulation program solves multiple instances of the optimization model successively in order to represent the progression of planning activities at the terminal.

Suggested Citation

  • J Fernando Alvarez & Tore Longva & Erna S Engebrethsen, 2010. "A methodology to assess vessel berthing and speed optimization policies," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 12(4), pages 327-346, December.
  • Handle: RePEc:pal:marecl:v:12:y:2010:i:4:p:327-346
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    Citations

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    Cited by:

    1. Liu, Baoli & Li, Zhi-Chun & Wang, Yadong, 2022. "A two-stage stochastic programming model for seaport berth and channel planning with uncertainties in ship arrival and handling times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    2. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.
    3. Alexander Senss & Onder Canbulat & Dogancan Uzun & Sefer Anil Gunbeyaz & Osman Turan, 2023. "Just in time vessel arrival system for dry bulk carriers," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-37, December.
    4. Branislav Dragović & Ernestos Tzannatos & Nam Kuy Park, 2017. "Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 4-34, March.
    5. Orlando Marco Belcore & Massimo Di Gangi & Antonio Polimeni, 2023. "Connected Vehicles and Digital Infrastructures: A Framework for Assessing the Port Efficiency," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    6. Ricardo Fukasawa & Qie He & Fernando Santos & Yongjia Song, 2018. "A Joint Vehicle Routing and Speed Optimization Problem," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 694-709, November.
    7. Jia, Shuai & Li, Chung-Lun & Xu, Zhou, 2020. "A simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container port," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 174-196.
    8. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship fleet deployment with container transshipment operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 470-484.
    9. Peter Andersson & Pernilla Ivehammar, 2017. "Dynamic route planning in the Baltic Sea Region – A cost-benefit analysis based on AIS data," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 631-649, December.
    10. Tianhao Shao & Weijie Du & Yun Ye & Haoqing Li & Jingxin Dong & Guiyun Liu & Pengjun Zheng, 2024. "A Novel Virtual Arrival Optimization Method for Traffic Organization Scenarios," Sustainability, MDPI, vol. 16(1), pages 1-17, January.
    11. Branislav Dragovic & Nenad Dj. Zrnic, 2011. "A Queuing Model Study of Port Performance Evolution," Analele Universitatii "Eftimie Murgu" Resita Fascicola de Inginerie, "Eftimie Murgu" University of Resita, vol. 2(XVIII), pages 65-76, December.
    12. Claudia Durán & Ivan Derpich & Raúl Carrasco, 2022. "Optimization of Port Layout to Determine Greenhouse Gas Emission Gaps," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    13. Du, Yuquan & Chen, Qiushuang & Quan, Xiongwen & Long, Lei & Fung, Richard Y.K., 2011. "Berth allocation considering fuel consumption and vessel emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1021-1037.
    14. Yuquan Du & Qiushuang Chen & Jasmine Siu Lee Lam & Ya Xu & Jin Xin Cao, 2015. "Modeling the Impacts of Tides and the Virtual Arrival Policy in Berth Allocation," Transportation Science, INFORMS, vol. 49(4), pages 939-956, November.
    15. Heimir Thorisson & Marwan Alsultan & Daniel Hendrickson & Thomas L. Polmateer & James H. Lambert, 2019. "Addressing schedule disruptions in business processes of advanced logistics systems," Systems Engineering, John Wiley & Sons, vol. 22(1), pages 66-79, January.
    16. Wang, Tingsong & Wang, Xinchang & Meng, Qiang, 2018. "Joint berth allocation and quay crane assignment under different carbon taxation policies," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 18-36.
    17. Wang, Shuaian & Meng, Qiang, 2012. "Sailing speed optimization for container ships in a liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 701-714.
    18. Sun, Qinghe & Meng, Qiang & Chou, Mabel C., 2021. "Optimizing voyage charterparty (VCP) arrangement: Laytime negotiation and operations coordination," European Journal of Operational Research, Elsevier, vol. 291(1), pages 263-270.
    19. Patrizia Serra & Gianfranco Fancello, 2020. "Towards the IMO’s GHG Goals: A Critical Overview of the Perspectives and Challenges of the Main Options for Decarbonizing International Shipping," Sustainability, MDPI, vol. 12(8), pages 1-32, April.
    20. Christiansen, Marielle & Fagerholt, Kjetil & Nygreen, Bjørn & Ronen, David, 2013. "Ship routing and scheduling in the new millennium," European Journal of Operational Research, Elsevier, vol. 228(3), pages 467-483.
    21. Assmann, Lisa & Andersson, Jonas & Eskeland, Gunnar S., 2015. "Missing in Action? Speed optimization and slow steaming in maritime shipping," Discussion Papers 2015/13, Norwegian School of Economics, Department of Business and Management Science.
    22. Fukasawa, Ricardo & He, Qie & Song, Yongjia, 2016. "A disjunctive convex programming approach to the pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 61-79.
    23. Eduardo Lalla-Ruiz & Stefan Voß & Christopher Expósito-Izquierdo & Belén Melián-Batista & J. Marcos Moreno-Vega, 2017. "A POPMUSIC-based approach for the berth allocation problem under time-dependent limitations," Annals of Operations Research, Springer, vol. 253(2), pages 871-897, June.

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